1. Field of the Invention
The present invention relates to an information processing apparatus, information processing method, and program and, in particular, an information processing apparatus, information processing method, and program suitable for use when an object on an image is recognized.
2. Description of the Related Art
In the related art, there is a technique for recognizing an object that is present on an image (a still picture or a moving picture). In an example of a method for this technique, an observation window is provided on an image, a characteristic amount of an area of the observation window is calculated, and the calculation result is compared with a characteristic amount of each pattern prepared in advance correspondingly to each of various existing objects to specify a best-matching pattern for each regional block.
However, when an object having a joint, for example, a human knee, is to be recognized from a moving picture, as depicted in FIG. 1, that object not only moves but makes more complicated motions. Therefore, when that object is a focus of attention and the state is compared with a state thereafter with reference to its state at a time t1, a correlation value gradually decreases as depicted in FIG. 2 in spite of the same object, thereby causing a pattern specified at each time to be varied. That is, for an image of a knee at the time t1, a pattern with a characteristic amount x1 (hereinafter also referred to as a pattern x1, and the same goes for other patterns) is specified. For an image of the knee at a time t2, a pattern x2 with a characteristic amount x2 is specified.
The characteristic amounts x1 to x4 of each pattern have different values as indicated by part A in FIG. 3. However, as described above, in order to make the pattern identified as the same object (the human knee), some transformation (such as a projection function) or grouping is used to gather the characteristic amounts x1 to x4 indicated by part A in FIG. 3 onto an invariable characteristic amount space indicated by part B in FIG. 3 to learn that the gathered amount represents a characteristic amount corresponding to the same object.
Thus, in the related art, as a method of grouping different patterns by learning, a method has been suggested in which a pattern is learnt by using a temporal change of an image extracted from an observation window provided so as to be positionally fixed on each frame of a learning image (a moving picture).
For example, in a method described in Learning Invariance from Transformation Sequences, Peter Foldiak, Neural Computation, 1991, a response of a pattern is temporally continued. Thus, a weight is learnt so that a response is made with the same pattern even when a slightly changed pattern comes.
In PHD Thesis of Dileep George, “How The Brain Might Work: A Hierarchical and Temporal Model for Learning and Recognition”, grouping of patterns is performed based on a temporal transition of a regional block.